Editors: Gyanendra Verma, Rajesh Doriya

Deep Learning: Theory, Architectures, and Applications in Speech, Image, and Language Processing

eBook: US $59 Special Offer (PDF + Printed Copy): US $101
Printed Copy: US $71
Library License: US $236
ISBN: 978-981-5079-22-7 (Print)
ISBN: 978-981-5079-21-0 (Online)
Year of Publication: 2023
DOI: 10.2174/97898150792101230101


This book is a detailed reference guide on deep learning and its applications. It aims to provide a basic understanding of deep learning and its different architectures that are applied to process images, speech, and natural language. It explains basic concepts and many modern use cases through fifteen chapters contributed by computer science academics and researchers. By the end of the book, the reader will become familiar with different deep learning approaches and models, and understand how to implement various deep learning algorithms using multiple frameworks and libraries.

The second part is dedicated to sentiment analysis using deep learning and machine learning techniques. This book section covers the experimentation and application of deep learning techniques and architectures in real-world applications. It details the salient approaches, issues, and challenges in building ethically aligned machines. An approach inspired by traditional Eastern thought and wisdom is also presented.

The final part covers artificial intelligence approaches used to explain the machine learning models that enhance transparency for the benefit of users. A review and detailed description of the use of knowledge graphs in generating explanations for black-box recommender systems and a review of ethical system design and a model for sustainable education is included in this section. An additional chapter demonstrates how a semi-supervised machine-learning technique can be used for cryptocurrency portfolio management.

The book is a timely reference for academicians, professionals, researchers and students at engineering and medical institutions working on artificial intelligence applications.

Audience: Computer science academicians, professionals, and students; researchers and engineers working on AI models.


Machine Learning proved its usefulness in many applications in Image Processing and Computer Vision, Medical Imaging, Satellite imaging, Remote Sensing, Surveillance, etc., over the past decade. At the same time, Machine Learning, particularly Artificial Neural Networks has evolved and demonstrated excellent performance over traditional machine learning algorithms. These methods are known as Deep Learning.

Nowadays, Deep Learning has become the researcher's first choice in contrast to traditional machine learning due to its apex performance on speech, image, and text processing. Deep learning algorithms provide efficient solutions to problems ranging from image and speech processing to text processing. The research on deep learning is getting enriched day by day as we witness new learning models.

Deep learning models significantly impacted speech, image, and text-domain and raised the performance bar substantially in many standard evaluations. Moreover, new challenges are easily tackled by utilizing deep learning, which older systems could not have handled. However, it is challenging to comprehend, let alone guide, the learning process in deep neural networks; there is an air of uncertainty about exactly what and how these networks learn.

This book aims to provide the audience with a basic understanding of deep learning and its different architectures. Background knowledge of machine learning helps explore various aspects of deep learning. By the end of the book, I hope that the reader understands different deep learning approaches, models, pre-trained models, and gains familiarity with implementing various deep learning algorithms using multiple frameworks and libraries.

Dr. Shitala Prasad
Scientist, Institute for Infocomm Research, A*Star
Singapore 138632